An individual wishing to find a piece of text that contains content similar to the content of another piece of text will often search for words that exist in both pieces. Likewise, an individual wishing to find relevant pieces of text will often search for specific words that are thought likely to indicate relevant content in text.
These techniques are central to methods for searching electronic records for relevant text but are also used for searching analog records. Examples of the latter would include concordances and library subject catalogs.
The limitations of existing methods are several: 1) words, and especially acronyms, may have multiple meanings; 2) large records, such as lengthy books and encyclopedias, might contain the specific words themselves but be largely irrelevant to the content sought or contain only a small section of relevant content in a much larger document; 3) text using synonyms of the specific words used for searching might not be found; and, 4) text written in foreign languages will be largely inaccessible.
In general, since there are many ways of expressing the same thought, words representing snippets of the text are not the ideal thing to look for. There is a compelling need for a user to find text that contains the meaning and thoughts that the user is looking for. There is a compelling need for a user to search and find relevant textual content that does not suffer from the drawbacks of the prior art and that allows a user who desires to find relevant content, and who tells the computer to look for some particular text, to infer the meaning of that text rather than to conduct a search that is confined to the literal words of the text themselves.